The background description provided herein is for the purpose of generally presenting the context of the disclosure. Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
In a data-flow programming paradigm, automated processes may be described in the form of data-flow graphs. To conduct performance analysis of data-flow programs, an associated data-flow graph may be partitioned into smaller, independent subgraphs which may be analyzed individually. Such partitioning may be desired because different partitions of a graph may have different performance characteristics and thus may benefit from different performance solutions. However, determining an optimal graph partitioning is typically a complex process which resists efficient computation, especially as graph sizes increase. Many typical graph partitioning processes do not scale well to large graph sizes.